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Collection of massive well-annotated samples is effective in improving object detection performance but is extremely laborious and costly.
SMOTE: synthetic minority over-sampling technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2002
Earlier work this paper cites.
Using color compatibility for assessing image realism
J.-F. Lalonde and A. A. Efros · 2007
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Learning from imbalanced data
H. He and E. A. Garcia · 2008
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Learning a discriminative model for the perception of realism in composite images
J.-Y. Zhu, P. Krahenbuhl, E. Shechtman, and A. A. Efros · 2015
Earlier work this paper cites.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Earlier work this paper cites.
Synthetic data for text localisation in natural images
A. Gupta, A. Vedaldi, and A. Zisserman · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Learning deep representation for imbalanced classification
C. Huang, Y. Li, C. Change Loy, and X. Tang · 2016
Earlier work this paper cites.
SSD: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Dataset augmentation in feature space
T. DeVries and G. W. Taylor · 2017
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Blitznet: A real-time deep network for scene understanding
N. Dvornik, K. Shmelkov, J. Mairal, and C. Schmid · 2017
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
D. Dwibedi, I. Misra, and M. Hebert · 2017
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DSSD: Deconvolutional single shot detector
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg · 2017
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Synthesizing training data for object detection in indoor scenes
G. Georgakis, A. Mousavian, A. C. Berg, and J. Kosecka · 2017
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mmdetection
K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang, C. C. Loy, and D. Lin · 2018
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Revisiting pre-training: An efficient training method for image classification
B. Cheng, Y. Wei, H. Shi, S. Chang, J. Xiong, and T. S. Huang · 2018
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Imbalanced deep learning by minority class incremental rectification
Q. Dong, S. Gong, and X. Zhu · 2018
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Modeling visual context is key to augmenting object detection datasets
N. Dvornik, J. Mairal, and C. Schmid · 2018
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On the importance of visual context for data augmentation in scene understanding
N. Dvornik, J. Mairal, and C. Schmid · 2018
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Robotic grasp detection using deep convolutional neural networks
S. Kumra and C. Kanan · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
Cited alongside, same era.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Cited alongside, same era.
Learning from simulated and unsupervised images through adversarial training
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Detecting and recognizing human-object interactions
G. Gkioxari, R. Girshick, P. Dollár, and K. He · 2018
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick · 2018
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Data augmentation by pairing samples for images classification
H. Inoue · 2018
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Cost-sensitive learning of deep feature representations from imbalanced data
S. H. Khan, M. Hayat, M. Bennamoun, F. A. Sohel, and R. Togneri · 2018
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Context-aware synthesis and placement of object instances
D. Lee, S. Liu, J. Gu, M.-Y. Liu, M.-H. Yang, and J. Kautz · 2018
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Learning to segment via cut-and-paste
T. Remez, J. Huang, and M. Brown · 2018
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Deep imbalanced attribute classification using visual attention aggregation
N. Sarafianos, X. Xu, and I. A. Kakadiaris · 2018
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SNIPER: Efficient multi-scale training
B. Singh, M. Najibi, and L. S. Davis · 2018
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Data augmentation using random image cropping and patching for deep cnns
R. Takahashi, T. Matsubara, and K. Uehara · 2018
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Class-balanced loss based on effective number of samples
Y. Cui, M. Jia, T.-Y. Lin, Y. Song, and S. Belongie · 2019
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Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition
R. Ranjan, V. M. Patel, and R. Chellappa · 2019
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Dynamic curriculum learning for imbalanced data classification
Y. Wang, W. Gan, W. Wu, and J. Yan · 2019
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